Solitude-Analysis
Tomasz Wlodarczyk
3/7/2023
1 Data upload
2 ND removed, Converted to a Dataframe
#replace ND with 0
tr <- matrix(data = NA, ncol = ncol(dt[,c(1:44)]), nrow=nrow(dt))
colnames(tr) <- colnames(dt[,c(1:44)])
for (i in 12:44)
{
tr[,c(i)] <- gsub(".*ND.*", 0, dt[,i])
}
for(i in 1:11)
{
tr[,c(i)] <- dt[,c(i)]
}
#transform to dataframe
tr <- as.data.frame.matrix(tr) #A correct command to change the dataset to dataframe after transformations
tr[,12:44] <- sapply(tr[,12:44],as.numeric) # Change a character to numeric (double)
typeof(tr$Cu_concentration) # confirm the value is no longer a character## [1] "double"3 Head of the dataset
head(tr)## Scientific_Name Group Plot Sample_Name Tube_No Type_of_Sample Total_Weight
## 1 Allionia incarnata G3 P1 P1_29_1 30;31 leaf 0.55
## 2 Allionia incarnata G3 P1 P1_30 28 leaf 0.166
## 3 Allionia incarnata G3 P1 P1_29_1_2 29 leaf 0.213
## 4 Allionia incarnata G3 P2 P2_E12 33;34 leaf 0.332
## 5 Allionia incarnata G3 P2 P2_E12_1 32 leaf 0.183
## 6 Allionia incarnata G3 P2 P2_E12_2 26 leaf 0.164
## Cup_No pXRF_measurement_ID File Material Cl_concentration
## 1 17 2126 Scan2126_19.14.hdx Plant 580
## 2 18 2127 Scan2127_19.19.hdx Plant 306
## 3 19 2128 Scan2128_19.25.hdx Plant 527
## 4 20 2129 Scan2129_19.30.hdx Plant 2576
## 5 21 2130 Scan2130_19.35.hdx Plant 4756
## 6 22 2131 Scan2131_19.39.hdx Plant 753
## Cl_uncertainty Ca_concentration Ca_uncertainty Ti_concentration
## 1 268 48102 704 143.0
## 2 178 22621 439 132.0
## 3 259 47147 698 124.0
## 4 462 37856 611 146.0
## 5 619 29095 530 90.3
## 6 234 6209 233 30.6
## Ti_uncertainty Cr_concentration Cr_uncertainty Mn_concentration
## 1 27.5 7.7 5.5 36.2
## 2 22.5 4.8 4.0 47.8
## 3 25.7 10.9 6.2 25.2
## 4 26.3 10.9 6.0 30.7
## 5 20.5 10.6 5.6 0.0
## 6 11.5 0.0 2.5 0.0
## Mn_uncertainty Fe_concentration Fe_uncertainty Ni_concentration
## 1 10.0 1037 33.0 0
## 2 10.7 1709 46.9 0
## 3 9.1 866 30.8 0
## 4 10.0 1320 35.8 0
## 5 5.8 668 26.6 0
## 6 5.1 371 23.4 0
## Ni_uncertainty Cu_concentration Cu_uncertainty Zn_concentration
## 1 1.2 37.5 4.5 0.0
## 2 1.2 8.1 3.2 11.8
## 3 1.2 35.9 5.0 0.0
## 4 1.2 47.1 5.1 0.0
## 5 1.2 26.1 4.6 0.0
## 6 1.5 7.9 3.2 0.0
## Zn_uncertainty As_concentration As_uncertainty Se_concentration
## 1 1.1 5.0 0.3 1.0
## 2 3.8 2.5 1.5 0.0
## 3 1.2 6.5 0.4 0.8
## 4 1.8 6.2 0.4 2.6
## 5 1.5 4.4 1.2 2.7
## 6 1.7 3.3 1.6 3.6
## Se_uncertainty Cd_concentration Cd_uncertainty Re_concentration
## 1 0.6 0.0 2.5 0.0
## 2 0.6 0.0 4.7 0.0
## 3 0.6 0.0 3.8 0.0
## 4 0.7 0.0 4.5 4.6
## 5 1.0 20.1 20.1 6.4
## 6 1.5 0.0 14.2 0.0
## Re_uncertainty Hg_concentration Hg_uncertainty Tl_concentration
## 1 1.5 0 0.4 0
## 2 2.1 0 0.6 0
## 3 1.9 0 0.5 0
## 4 2.6 0 0.5 0
## 5 3.4 0 0.7 0
## 6 2.9 0 0.8 0
## Tl_uncertainty Pb_concentration Pb_uncertainty Substrate_RT
## 1 0.6 0 0.9 0.05758524
## 2 0.7 0 2.5 0.01436361
## 3 0.5 0 1.0 0.03456240
## 4 0.5 0 0.8 0.05420076
## 5 0.9 0 1.9 0.02482426
## 6 1.2 0 2.8 0.011043404 Subsets and wrangling
#Filtering with tydeverse library
dt_plants <- filter(tr, Scientific_Name != 'QA_Sample')
P1 <- filter(dt_plants, Plot == "P1")
P2 <- filter(dt_plants, Plot == "P2")
P5 <- filter(dt_plants, Plot == "P5")
P6 <- filter(dt_plants, Plot == "P6")
P125 <- filter(dt_plants, Plot != "P6")
Se_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Populus fremontii' | Scientific_Name == 'Senegalia (Acacia) greggii' )
Re_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Baccharis sarothroides' | Scientific_Name == 'Senegalia (Acacia) greggii'| Scientific_Name == 'Nultuma (Prosopis) velutina' | Scientific_Name == 'Mimosa biuncifera (=aculeaticarpa)' | Scientific_Name == 'Fraxinus velutina'| Scientific_Name == 'Datura wrightii' )
# Dropping uncertainty columns for PCA analysis
dt_plants_nounc = select(dt_plants, -Cl_uncertainty,-Ca_uncertainty, -Ti_uncertainty,
-Cr_uncertainty, -Mn_uncertainty, -Fe_uncertainty, -Ni_uncertainty, -Cu_uncertainty,
-Zn_uncertainty, -As_uncertainty, -Se_uncertainty, -Cd_uncertainty, -Re_uncertainty, -Hg_uncertainty,
-Tl_uncertainty, -Pb_uncertainty, -Substrate_RT)
dt_plants_nounc = select(dt_plants_nounc, -Hg_concentration, -Tl_concentration, -Pb_concentration, -Ni_concentration)
#Filtering plants By Plot with subset function
dt_plants_nounc1 <- subset(dt_plants_nounc, Plot=="P1")
dt_plants_nounc2 <- subset(dt_plants_nounc, Plot=="P2")
dt_plants_nounc5 <- subset(dt_plants_nounc, Plot=="P5")
dt_plants_nounc6 <- subset(dt_plants_nounc, Plot=="P6")
dt_plants_nounce15 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5")
dt_plants_nounce125 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5" | Plot=="P2")
#Removing _concentration from column names
colnames(dt_plants_nounce125)[12] <- "Cl"
colnames(dt_plants_nounce125)[13] <- "Ca"
colnames(dt_plants_nounce125)[14] <- "Ti"
colnames(dt_plants_nounce125)[15] <- "Cr"
colnames(dt_plants_nounce125)[16] <- "Mn"
colnames(dt_plants_nounce125)[17] <- "Fe"
colnames(dt_plants_nounce125)[18] <- "Cu"
colnames(dt_plants_nounce125)[19] <- "Zn"
colnames(dt_plants_nounce125)[20] <- "As"
colnames(dt_plants_nounce125)[21] <- "Se"
colnames(dt_plants_nounce125)[22] <- "Cd"
colnames(dt_plants_nounce125)[23] <- "Re"
colnames(dt_plants_nounc6)[12] <- "Cl"
colnames(dt_plants_nounc6)[13] <- "Ca"
colnames(dt_plants_nounc6)[14] <- "Ti"
colnames(dt_plants_nounc6)[15] <- "Cr"
colnames(dt_plants_nounc6)[16] <- "Mn"
colnames(dt_plants_nounc6)[17] <- "Fe"
colnames(dt_plants_nounc6)[18] <- "Cu"
colnames(dt_plants_nounc6)[19] <- "Zn"
colnames(dt_plants_nounc6)[20] <- "As"
colnames(dt_plants_nounc6)[21] <- "Se"
colnames(dt_plants_nounc6)[22] <- "Cd"
colnames(dt_plants_nounc6)[23] <- "Re"5 Data Visualization
5.1 Boxplots - Cu - All plots
Cu_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Cu_AllPlots
5.2 Boxplots - Re - All Plots
Re_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
#ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Re_AllPlots
5.3 Boxplots - Re - Selected species
Re_box <- ggplot(Re_best, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, fill=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 45))+
geom_jitter(color="#85b8bc", size=2, alpha=0.9) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
#scale_fill_manual(values = c("", "", "", "", "", "","" ))
scale_fill_manual(values = c("#4b2866", "#c7abdd", "#a578c9", "#381e4c", "#8347b2", "#5d327f","#251433" ))
#scale_fill_brewer(palette = "Greens")
Re_box
5.4 Boxplots - Zn - All Plots
Zn_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Zn_concentration, FUN = median), y = Zn_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
#ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Zn_AllPlots
5.5 Boxplots - Se - All Plots
Se_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Se_concentration, FUN = median), y = Se_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
ylim(0,60)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Se_AllPlots
5.6 Boxplots - Se - Selected species
Se_box <- ggplot(Se_best, aes(x = reorder(Scientific_Name, Se_concentration, FUN=median), y = Se_concentration, fill=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 45))+
geom_jitter(color="#85b8bc", size=3, alpha=0.9) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values = c("#251433", "#c7abdd", "#8347b2"))
Se_box
5.7 Boxplots - Cu - Plot 6
Plants collected at the plot 6 were growing directly on the mine tailings that were exposed on the area of 100 x 100 m. Shrubs were also collected in the close vicinity to the tailings given their rooting depths.
Plot 6
Cu_Plot6 <- ggplot(P6, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1.6) +
ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08"))
Cu_Plot6